Optical measurements of the edges of manufactured objects are of great value in industry, but in practice such measurements are often difficult to accomplish with great accuracy or confidence. Industrially manufactured objects such as automotive metal parts, plastic parts and so on typically vary in their optical reflectance properties, some being more reflective than others for a given incident beam intensity. Furthermore, similar parts that are manufactured from the same specifications, for example a series of car door panels of the same model, also vary from one another in terms of the local reflectance properties that these parts exhibit near the edges of the parts. These variations are usually attributable to the manufacturing process and variations in the raw material properties between the parts. There are often part to part and batch to batch manufacturing variations due to progressive wear and tear of the manufacturing tools, differences between several production lines that manufacture the same parts, and so on. For example, the sheet metal used for the manufacture of many automotive parts is comprised of carbon and ferrite: small differences in the ratio of carbon to ferrite, and/or in the thickness of the sheet metal, coupled with different coatings and treatments that may be applied can lead to relatively large variations in the optical reflectance characteristics of the manufactured parts. Further, when attempting to generate an image of an edge-comprising part, the edge reflectance characteristics of the part, which may include for example specular reflections, surface texture and other forms of optical interferences near the edge, can cause the edge or part thereof in the image to appear shifted relative to where the true mechanical edge of the part should appear in the image.
Machine vision systems often utilize different illumination schemes to overcome such problems. For example, use of polarized illumination helps to reduce specular reflections that often result in image saturation, reducing the image saturation and thus providing a more accurate image of the edges. Alternatively, the image acquisition system may be provided with filters of different spectral bands to filter out of the image texture that interferes with the edge measurement. Back illumination methods are based on illuminating the object from behind, and this has the result of inverting the polarities in the image, i.e. the object appears as dark and the background as bright in the image. While effective to varying degrees, these methods nevertheless add significant complexity to the image acquisition systems. Moreover, it is not possible in all applications to apply such techniques, for example when the objects being imaged are large objects such as for example some automotive parts, which would require large and complex back illumination systems.